Improving the accuracy of top-N recommendation using a preference model

In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well suited for representing qualitative user preferences. In particular, it can be easily implemented with less than 100 lines of codes on top of existing CF algorithms such as user based, item-based, and matrix-factorization-based algorithms. When our preference model is combined to three kinds of CF algorithms, experimental results demonstrate that the preference model can improve the accuracy of all existing CF algorithms such as ATOP and NDCG@25 by 3-24% and 6-98%, respectively. (C) 2016 Elsevier Inc. All rights reserved.This work was supported by Hankuk University of Foreign Studies Research Fund of 2014 (Jongwuk Lee). His research was in part supported by NSF CNS-1422215 and Samsung 2015 GRO-175998 awards (Dongwon Lee). This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) (NRF-2014R1A2A1A10054151) and by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2015-H8501-15-1013) (Sang-Wook Kim)